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Explore how statistical discrimination creates self-reinforcing cycles in algorithmic decision-making through this 16-minute conference talk that examines the feedback mechanisms between biased predictions and discriminatory outcomes. Learn about the mathematical foundations of statistical discrimination and discover how initial biases in data or algorithms can perpetuate and amplify over time through feedback loops. Understand the implications for resource allocation systems, content moderation platforms, and participation mechanisms in digital environments. Gain insights into the theoretical framework that explains why discrimination persists even when algorithms are designed to be fair, and examine real-world examples where statistical discrimination has created lasting inequitable outcomes across different demographic groups.
